The advent of the Information Age has brought with it an increase in the accessibility of data, accompanied by schemes for searching that data. One searching for specific data through the Internet or in other information systems using any of many search engines available is often presented with an lengthy list of documents which may or may not contain the data for which he was searching. Reading through such a lengthy list is undesirably time consuming.
To reduce the time needlessly wasted in such reading, a variety of technologies have been presented for summarizing multiple documents to express a theme central to these documents. However, all of these technologies are inherently limited in some aspect. Some are able to search only a specific domain of knowledge and are therefore difficult to implement for different applications. Some, without radical modification, can only search documents composed in certain languages. Some use deep language parsing, statistical, or term-vector based techniques, resulting in longer waits for search results and greater demands on computing resources. Almost all generate summaries by merely concatenating together text segments containing some keyword, often producing results which are incohesive due to anaphoric ambiguity. None use real natural language analyzing techniques. A method for summarizing multiple documents while avoiding these limitations is desirable.